| Summary: | Wearable biometric tracking devices are becoming increasingly common, providing users with physiological metrics such as heart rate variability (HRV) and skin conductance. We hypothesize that these metrics can be used as inputs for machine learning models to detect independent variables, such as target prevalence or hours awake, objective task performance, and metacognitive states. Over the course of 1–25 h awake, 40 participants completed four sessions of a simulated mine hunting task while non-invasive wearables collected physiological and behavioral data. The collected data were used to generate multiple machine learning models to detect the independent variables of the experiment (e.g., time awake and target prevalence), objective task performance, or metacognitive states. The strongest generated model was the time awake detection model (area under the curve = 0.92). All other models performed much closer to chance (area under the curve = 0.57–0.66), suggesting the model architecture used in this paper can detect time awake but falls short in other domains.
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